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Open Access Publications from the University of California

Scholarly Works (21 results)

  • Article
  • Peer Reviewed
UC Irvine Previously Published Works (2010)
Background

With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases.

Results

We developed TabSQL, a MySQL-based application tool, for viewing, filtering and querying data files with large numbers of rows. TabSQL provides functions for downloading and installing table files from public databases including the Gene Ontology database (GO), the Ensembl databases, and genome databases from the UCSC genome bioinformatics site. Any other database that provides tab-delimited flat files can also be imported. The downloaded gene annotation tables can be queried together with users' data in TabSQL using either a graphic interface or command line.

Conclusions

TabSQL allows queries across the user's data and public databases without programming. It is a convenient tool for biologists to annotate and enrich their data.

    Cover page: TabSQL: a MySQL tool to facilitate mapping user data to public databasesCreative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2012)
      Cover page: Evaluation of the Role of Functional Constraints on the Integrity of an Ultraconserved Region in the Genus DrosophilaCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      LBL Publications (2017)
      We compute the fermion spin distribution in the vortical fluid created in off-central high energy heavy-ion collisions. We employ the event-by-event (3+1)D viscous hydrodynamic model. The spin polarization density is proportional to the local fluid vorticity in quantum kinetic theory. As a result of strong collectivity, the spatial distribution of the local vorticity on the freeze-out hyper-surface strongly correlates to the rapidity and azimuthal angle distribution of fermion spins. We investigate the sensitivity of the local polarization to the initial fluid velocity in the hydrodynamic model and compute the global polarization of Λ hyperons by the AMPT model. The energy dependence of the global polarization agrees with the STAR data.
        Cover page: Local and global Λ polarization in a vortical fluid
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2014)
        MicroRNAs (miRNAs) are endogenous RNA molecules that regulate gene expression posttranscriptionally. To date, the emergence of miRNAs and their patterns of sequence evolution have been analyzed in great detail. However, the extent to which miRNA expression levels have evolved over time, the role different evolutionary forces play in shaping these changes, and whether this variation in miRNA expression can reveal the interplay between miRNAs and mRNAs remain poorly understood. This is especially true for miRNA expressed during key developmental transitions. Here, we assayed miRNA expression levels immediately before (≥18BPF [18 h before puparium formation]) and after (PF) the increase in the hormone ecdysone responsible for triggering metamorphosis. We did so in four strains of Drosophila melanogaster and two closely related species. In contrast to their sequence conservation, approximately 25% of miRNAs analyzed showed significant within-species variation in male expression levels at ≥18BPF and/or PF. Additionally, approximately 33% showed modifications in their pattern of expression bias between developmental timepoints. A separate analysis of the ≥18BPF and PF stages revealed that changes in miRNA abundance accumulate linearly over evolutionary time at PF but not at ≥18BPF. Importantly, ≥18BPF-enriched miRNAs showed the greatest variation in expression levels both within and between species, so are the less likely to evolve under stabilizing selection. Functional attributes, such as expression ubiquity, appeared more tightly associated with lower levels of miRNA expression polymorphism at PF than at ≥18BPF. Furthermore, ≥18BPF- and PF-enriched miRNAs showed opposite patterns of covariation in expression with mRNAs, which denoted the type of regulatory relationship between miRNAs and mRNAs. Collectively, our results show contrasting patterns of functional divergence associated with miRNA expression levels during Drosophila ontogeny.
          Cover page: Functional Divergence of the miRNA Transcriptome at the Onset of Drosophila Metamorphosis
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2010)
          Most current microarray oligonucleotide probe design strategies are based on probe design factors (PDFs), which include probe hybridization free energy (PHFE), probe minimum folding energy (PMFE), dimer score, hairpin score, homology score and complexity score. The impact of these PDFs on probe performance was evaluated using four sets of microarray comparative genome hybridization (aCGH) data, which included two array manufacturing methods and the genomes of two species. Since most of the hybridizing DNA is equimolar in CGH data, such data are ideal for testing the general hybridization properties of almost all candidate oligonucleotides. In all our data sets, PDFs related to probe secondary structure (PMFE, hairpin score and dimer score) are the most significant factors linearly correlated with probe hybridization intensities. PHFE, homology and complexity score are correlating significantly with probe specificities, but in a non-linear fashion. We developed a new PDF, pseudo probe binding energy (PPBE), by iteratively fitting dinucleotide positional weights and dinucleotide stacking energies until the average residue sum of squares for the model was minimized. PPBE showed a better correlation with probe sensitivity and a better specificity than all other PDFs, although training data are required to construct a PPBE model prior to designing new oligonucleotide probes. The physical properties that are measured by PPBE are as yet unknown but include a platform-dependent component. A practical way to use these PDFs for probe design is to set cutoff thresholds to filter out bad quality probes. Programs and correlation parameters from this study are freely available to facilitate the design of DNA microarray oligonucleotide probes.
            Cover page: Evaluating oligonucleotide properties for DNA microarray probe designCreative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2011)
            Identification of gene expression changes that improve prediction of survival time across all glioma grades would be clinically useful. Four Affymetrix GeneChip datasets from the literature, containing data from 771 glioma samples representing all WHO grades and eight normal brain samples, were used in an ANOVA model to screen for transcript changes that correlated with grade. Observations were confirmed and extended using qPCR assays on RNA derived from 38 additional glioma samples and eight normal samples for which survival data were available. RNA levels of eight major mitotic spindle assembly checkpoint (SAC) genes (BUB1, BUB1B, BUB3, CENPE, MAD1L1, MAD2L1, CDC20, TTK) significantly correlated with glioma grade and six also significantly correlated with survival time. In particular, the level of BUB1B expression was highly correlated with survival time (p<0.0001), and significantly outperformed all other measured parameters, including two standards; WHO grade and MIB-1 (Ki-67) labeling index. Measurement of the expression levels of a small set of SAC genes may complement histological grade and other clinical parameters for predicting survival time.
              Cover page: The Accuracy of Survival Time Prediction for Patients with Glioma Is Improved by Measuring Mitotic Spindle Checkpoint Gene ExpressionCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Santa Cruz Previously Published Works (2022)
              Utilizing reversible lattice oxygen redox (OR) in battery electrodes is an essential strategy to overcome the capacity limitation set by conventional transition metal redox. However, lattice OR reactions are often accompanied with irreversible oxygen oxidation, leading to local structural transformations and voltage/capacity fading. Herein, it is proposed that the reversibility of lattice OR can be remarkably improved through modulating transition metal-oxygen covalency for layered electrode of Na-ion batteries. By developing a novel layered P2-Na0.6 Mg0.15 Mn0.7 Cu0.15 O2 electrode, it is demonstrated that the highly electronegative Cu dopants can improve the lattice OR reversibility to 95% compared to 73% for Cu-free counterpart, as directly quantified through high-efficiency mapping of resonant inelastic X-ray scattering. Crucially, the large energetic overlap between Cu 3d and O 2p states dictates the rigidity of oxygen framework, which effectively mitigates the structural distortion of local oxygen environment upon (de)sodiation and leads to the enhanced lattice OR reversibility. The electrode also exhibits a completely solid-solution reaction with an ultralow volume change of only 0.45% and a reversible metal migration upon cycling, which together ensure the improved electrochemical performance. These results emphasize the critical role of transition metal-oxygen covalency for enhancing the reversibility of lattice OR toward high-capacity electrodes employing OR chemistry.
                Cover page: Enhancing the Reversibility of Lattice Oxygen Redox Through Modulated Transition Metal–Oxygen Covalency for Layered Battery ElectrodesCreative Commons 'BY-NC' version 4.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2012)
                • 1 supplemental PDF
                • 5 supplemental files
                Cover page: High-throughput comparison of gene fitness among related bacteriaCreative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2012)
                Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.
                  Cover page: Expression Changes in the Stroma of Prostate Cancer Predict Subsequent RelapseCreative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  ICTS Publications (2013)

                  In case-control profiling studies, increasing the sample size does not always improve statistical power because the variance may also be increased if samples are highly heterogeneous. For instance, tumor samples used for gene expression assay are often heterogeneous in terms of tissue composition or mechanism of progression, or both; however, such variation is rarely taken into account in expression profiles analysis. We use a prostate cancer prognosis study as an example to demonstrate that solely recruiting more patient samples may not increase power for biomarker detection at all. In response to the heterogeneity due to mixed tissue, we developed a sample selection strategy termed Stepwise Enrichment by which samples are systematically culled based on tumor content and analyzed with t-test to determine an optimal threshold for tissue percentage. The selected tissue-percentage threshold identified the most significant data by balancing the sample size and the sample homogeneity; therefore, the power is substantially increased for identifying the prognostic biomarkers in prostate tumor epithelium cells as well as in prostate stroma cells. This strategy can be generally applied to profiling studies where the level of sample heterogeneity can be measured or estimated.

                    Cover page: A sample selection strategy to boost the statistical power of signature detection in cancer expression profile studies.Creative Commons 'BY' version 4.0 license
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